An unstated assumption in much of the work in imitation learning (IL) is that the learner and the expert have access to the same state information. With powerful enough statistical models, this assumption places us in the realizable setting – i.e. the imitator can actually behave like the expert. In practice however, an expert might have more information than the learner does. For example, an expert policy might be trained in simulation with privileged access to state before being used to supervise a learner policy that only has access to a subset of features. This recipe has enjoyed success in domains from motion planning with obstacles [1]}, to autonomous driving [2]}, to legged locomotion [3]}, [4]}.
